{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib\n",
    "# this is our global size of label text  on the plots\n",
    "matplotlib.rc('xtick', labelsize=20) \n",
    "matplotlib.rc('ytick', labelsize=20) \n",
    "\n",
    "# This line ensures that the plot is displayed\n",
    "# inside the notebook\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((8693, 14), (4277, 13))"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train = pd.read_csv('../input/train.csv')\n",
    "test = pd.read_csv('../input/test.csv')\n",
    "\n",
    "train.shape, test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((12970, 13), 12970)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "temp = train.drop('Transported', axis=1)\n",
    "full = pd.concat([temp, test])\n",
    "full.shape, 8693+4277"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>HomePlanet</th>\n",
       "      <th>CryoSleep</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Destination</th>\n",
       "      <th>Age</th>\n",
       "      <th>VIP</th>\n",
       "      <th>RoomService</th>\n",
       "      <th>FoodCourt</th>\n",
       "      <th>ShoppingMall</th>\n",
       "      <th>Spa</th>\n",
       "      <th>VRDeck</th>\n",
       "      <th>Name</th>\n",
       "      <th>Transported</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0001_01</td>\n",
       "      <td>Europa</td>\n",
       "      <td>False</td>\n",
       "      <td>B/0/P</td>\n",
       "      <td>TRAPPIST-1e</td>\n",
       "      <td>39.0</td>\n",
       "      <td>False</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Maham Ofracculy</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0002_01</td>\n",
       "      <td>Earth</td>\n",
       "      <td>False</td>\n",
       "      <td>F/0/S</td>\n",
       "      <td>TRAPPIST-1e</td>\n",
       "      <td>24.0</td>\n",
       "      <td>False</td>\n",
       "      <td>109.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>549.0</td>\n",
       "      <td>44.0</td>\n",
       "      <td>Juanna Vines</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0003_01</td>\n",
       "      <td>Europa</td>\n",
       "      <td>False</td>\n",
       "      <td>A/0/S</td>\n",
       "      <td>TRAPPIST-1e</td>\n",
       "      <td>58.0</td>\n",
       "      <td>True</td>\n",
       "      <td>43.0</td>\n",
       "      <td>3576.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6715.0</td>\n",
       "      <td>49.0</td>\n",
       "      <td>Altark Susent</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  PassengerId HomePlanet CryoSleep  Cabin  Destination   Age    VIP  \\\n",
       "0     0001_01     Europa     False  B/0/P  TRAPPIST-1e  39.0  False   \n",
       "1     0002_01      Earth     False  F/0/S  TRAPPIST-1e  24.0  False   \n",
       "2     0003_01     Europa     False  A/0/S  TRAPPIST-1e  58.0   True   \n",
       "\n",
       "   RoomService  FoodCourt  ShoppingMall     Spa  VRDeck             Name  \\\n",
       "0          0.0        0.0           0.0     0.0     0.0  Maham Ofracculy   \n",
       "1        109.0        9.0          25.0   549.0    44.0     Juanna Vines   \n",
       "2         43.0     3576.0           0.0  6715.0    49.0    Altark Susent   \n",
       "\n",
       "   Transported  \n",
       "0        False  \n",
       "1         True  \n",
       "2        False  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1    4378\n",
       " 0    4315\n",
       " Name: Transported, dtype: int64,\n",
       " 1    0.503624\n",
       " 0    0.496376\n",
       " Name: Transported, dtype: float64)"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.Transported.value_counts(),  train.Transported.value_counts(normalize=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Count')"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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qXbt2OT5nw4YN6tAhI11369ZNK1eutOstPj5eTZo00e+//67y5cvrt99+U4UKFXLUmTBhgiZPnixJevPNN/XSSy/ZjW/btk0RERFKS0tTRESENm3a5PL7z44VNqD4sMIGlFyssLmQW1iTpJ49e6pOnTqSpO+++y7H+IIFC5SQkCBJmjp1ql1Yy/yMOXPmmJ/11ltvOfyct99+W5Lk7u5uNz9T5cqVNXXqVEnSpUuXtGDBghw1rl+/rhkzZkiS6tWrp1GjRuWYc99992nAgAGSpM2bN2vXrl1OvnMAAFAaWS6w5ZWfn58k6erVqznGoqOjJWWcF/bQQw853D8kJEQdO3aUJH377bdKSkqyG09KStK332b8F2fHjh0VEhLisM5DDz1kpuKVK1fmGN+4caMZHvv37y83N8c/8qeeesrcdlQHAACUXrdlYPvll1/Mc73q1q1rN3bt2jXt3LlTknTvvffKw8PDaZ2IiAhJUmpqqmJjY+3Gdu3apWvXrtnNc8TDw0MtW7Y097l+/brd+JYtW3J8niNNmzaVt7e3JGnr1q1O5wEAgNLntglsycnJ+vXXXzVt2jTzfC9JGjlypN28Q4cO6caNG5Jyhrnsso4fOHDAbmz//v0O57mqk5aWluMq0LzWcXd3V3h4uMNeAABA6WbpwLZ48WLZbDbZbDb5+Piodu3aGjVqlM6cOSNJGjt2rB5//HG7fU6ePGluOzuMmSk0NNTcPnHiRJHW8fHxUfny5fNU59y5c0pNTXU5FwAAlB7uxd1AQdxzzz2aP3++mjVrlmMs67lovr6+Luv4+PiY25cvXy7SOrnVcFTH09PT4bzU1FS7QJeYmJhrbQAAcPuy9Apbz549tXfvXu3du1c7d+7U0qVLFRkZqR9//FF9+vTR6tWrc+yT9SIEV+evSbILRCkpKUVaJ7caudXJasqUKQoICDC/sq7wAQCAksfSga18+fJq0KCBGjRooGbNmumxxx7TihUr9PHHH+u3335Tjx49tHjxYrt9sj61IPOiAWeyrlJ5eXkVaZ3cauRWJ6tx48YpISHB/Mp+GBYAAJQslg5szjz55JN65JFHlJ6ermeffVYXLlwwxzJv9yHlPDyZ3ZUrV8zt7IcsC7tObjVyq5OVp6en/P397b4AAEDJdVsGNknq0aOHpIyQ89VXX5nvZ71AIOuFA45kXZnKflixsOtcuXJFly5dylOdwMBAp+evAQCA0ue2DWyBgYHm9vHjx83t2rVrm08kOHjwoMsaWcfr1atnN1a/fn2H81zVcXd3V61atQpUJy0tTUeOHHHYCwAAKN1u28D2xx9/mNtZDx96eHioefPmkqTvv//e5bljmzdvlpRxiLFp06Z2Y82aNTMvFMic58i1a9e0fft2c5+yZcvajbdu3TrH5zkSGxtrHhJt1aqV03kAAKD0uW0D27Jly8zthg0b2o317NlTUsbtLrI/GD7TyZMn9c0330iSOnToYHfOmpRx7lnmg9+/+eYbp4dFV6xYYd5WI/OB9Fm1a9dOAQEBkqSPPvpIhmE4rJP14glHdQAAQOllucC2ePFih88Hzerdd9/V2rVrJUk1atRQmzZt7MYHDhxohqSxY8fq/PnzduM3btzQM888Yz4R4aWXXnL4OS+++KKkjMOVw4YNM+dnio+P15gxYyRlXNE6cODAHDU8PDz03HPPScp4gkHmA+Wz+v7777Vw4UJJGY+vcnR/OQAAUHrZDGdLPsWkevXqSkpK0sMPP6zWrVsrLCxMvr6+SkpK0t69e/XJJ5+Yz9r08PDQmjVrzIe4ZzVv3jz97W9/kySFhYXplVdeUcOGDXXq1ClNnz5dGzdulCT16dNH//73v53206dPH3366aeSpPbt22vkyJEKDg7W3r179dprr5nnnc2bN0+DBw92WCMpKUlNmzbVoUOHJEmDBw/WY489Ji8vL23cuFGvv/66Ll++LC8vL23btk333HNPvn5miYmJCggIUEJCQpFeMTp3V0yR1QZuV0ObtS3uFgpFSsq3xd0CYDleXh2KtH5+/n5bMrBlvYjAmZCQEC1atEh//vOfnc559dVXNXnyZKeHITt37qzly5fb3XMtu5SUFPXq1ctc0cvOzc1Nf//73zVx4kSX/R4+fFidO3fO8azRTP7+/vrkk0/UtWtXl3UcIbABxYfABpRcBDYXfvnlF61Zs0Zbt27V4cOHdebMGZ0/f15eXl6qUqWK7rnnHnXt2lW9e/eWt7d3rvW2bdum2bNn67vvvtOZM2dUvnx5/elPf9LTTz+tPn365Lmvf//731q8eLF++uknXbp0SXfccYfatGmjZ599Vvfee2+ealy5ckWzZ8/WsmXLdPjwYV27dk2hoaHq3LmzRowYoWrVquW5n6wIbEDxIbABJReBDYWKwAYUHwIbUHJZKbBZ7qIDAAAA2COwAQAAWByBDQAAwOIIbAAAABZHYAMAALA4AhsAAIDFEdgAAAAsjsAGAABgcQQ2AAAAiyOwAQAAWByBDQAAwOIIbAAAABZHYAMAALA4AhsAAIDFEdgAAAAsjsAGAABgcQQ2AAAAiyOwAQAAWByBDQAAwOIIbAAAABZHYAMAALA4AhsAAIDFFTiw3X///fr222+djm/cuFH3339/QcsDAADgfwoc2DZt2qQzZ844HT979qw2b95c0PIAAAD4n5s6JGqz2ZyOHT58WH5+fjdTHgAAAJLc8zP5o48+0kcffWS+/uc//6kPPvggx7xLly7pv//9rzp37nzzHQIAAJRy+QpsycnJOnfunPk6KSlJbm72i3Q2m00+Pj7629/+pgkTJhROlwAAAKVYvgLb0KFDNXToUElSjRo19N5776l79+5F0hgAAAAy5CuwZXX06NHC7AMAAABOFDiwZUpKStLx48d18eJFGYaRY7xt27Y3+xEAAAClWoEDW3x8vIYPH67ly5frxo0bOcYNw5DNZnM4BgAAgLwrcGAbPHiwvvzySz333HNq06aNKlSoUJh9AQAA4H8KHNj+85//6Pnnn9ebb75ZmP0AAAAgmwLfONfb21vVq1cvxFYAAADgSIEDW9++fbVy5crC7AUAAAAOFPiQaK9evbR582b95S9/0eDBgxUaGqoyZcrkmPd///d/N9UgAABAaVfgwNa6dWtz++uvv84xzlWiAAAAhaPAge3DDz8szD4AAADgRIEDW//+/QuzDwAAADhR4IsOAAAAcGsUeIXtr3/9a65zbDabFi5cWNCPAAAAgG4isG3YsEE2m83uvRs3buj06dO6ceOGAgMD5ePjc9MNAgAAlHYFDmzHjh1z+P7169c1b948TZ8+3eHVowAAAMifQj+HrWzZsnr22Wf1wAMP6Nlnny3s8gAAAKVOkV108Kc//UkxMTFFVR4AAKDUKLLA9vXXX8vb27uoygMAAJQaBT6H7R//+IfD9y9duqSYmBjt2bNHY8eOLXBjAAAAyFDgwDZx4kSH71eoUEFhYWF6//33NWjQoIKWBwAAwP8UOLClp6cXZh8AAABwgicdAAAAWFyBV9gybd68WWvWrNHx48clSdWqVVOXLl0UERFx080BAADgJgLbtWvX1KdPH0VHR8swDJUvX15SxkUH77zzjiIjI7V06VKVLVu2sHoFAAAolQp8SHTSpElauXKlRo0apdOnT+vChQu6cOGC4uLi9OKLL2rFihVOryQFAABA3hU4sP373/9W//799eabb+qOO+4w369SpYqmTp2qfv36acmSJYXSJAAAQGlW4MB2+vRptWjRwul4ixYtFBcXV9DyAAAA+J8CB7aQkBBt2rTJ6fjmzZsVEhJS0PIAAAD4nwIHtv79+ysqKkp/+9vf9Msvv+jGjRtKT0/XL7/8oqFDh2rZsmV66qmnCrFVAACA0qnAV4m+/PLLOnLkiObPn68PPvhAbm4Z2S89PV2GYah///56+eWXC61RAACA0qrAga1MmTJavHixXnjhBa1du9buPmydO3dWo0aNCq1JAACA0ixfh0SvXr2qv/3tb5o5c6b5XqNGjTR27FjNnTtXc+fO1dixY7Vp0yY988wzun79eoGaio2N1T/+8Q898MADCgkJkaenp3x9fVW7dm09/fTT2rJlS77qrVu3TpGRkWatkJAQRUZGat26dXmukZaWpvfff19t2rRRYGCgvLy8FBYWpiFDhujnn3/Oc534+HhNmDBBjRo1kr+/v/z9/dWoUSNNmDBB58+fz9f3BQAASgebYRhGXifPmDFDo0eP1v79+1WzZk2n844cOaK7775b7777roYOHZqvhtq2bavvvvsu13n9+vXTBx98IA8PD6dz0tPTNXjwYC1cuNDpnIEDB2revHnmIV1H4uPj1blzZ+3atcvhuKenp2bNmqWBAwe67HnHjh3q2bOn06tnq1atqujoaDVv3txlnewSExMVEBCghIQE+fv752vf/Ji7K6bIagO3q6HN2hZ3C4UiJeXb4m4BsBwvrw5FWj8/f7/ztcIWFRWlhx9+2GVYk6SwsDA98sgjWrp0aX7KS5JOnTolSQoODtaIESP0+eefa+fOnfr+++81bdo03XnnnZKkjz/+ONeLGl555RUzrDVu3FhLly7Vzp07tXTpUjVu3FiStGDBAo0fP95pjRs3bigyMtIMaw899JDWrVunHTt2aMaMGapSpYpSU1M1ZMgQlyt2J06cULdu3RQXFyd3d3eNHj1aMTExiomJ0ejRo+Xu7q7Tp0+rW7duOnnyZJ5/XgAAoOTL1wpbQECA3njjjTytms2dO1fjxo3TpUuX8tVQ165d1a9fPz388MMqU6ZMjvH4+Hi1atVKhw4dkpRx+5C2bXP+F+6hQ4d09913Ky0tTU2bNlVMTIy8vLzM8eTkZEVERCg2Nlbu7u46cOCAwsPDc9RZtGiRBgwYIEl65plnNHv2bLvxw4cPq0mTJkpMTFR4eLgOHDggd/ecpwZmvZFwVFSUHnnkEbvxqKgoPfroo5IyrsBdvHixqx+THVbYgOLDChtQct22K2zXrl1zeQgyKw8PD6WmpuanvCRp9erV6t27t8OwJkmVK1fWO++8Y77+/PPPHc6bPn260tLSJEkzZ860C2uS5O3tbZ6Ll5aWpnfffddhnbfffluSVLFiRb311ls5xsPDwzVu3DhJGeFt5cqVOebExcXpk08+kSR16tQpR1iTpN69e6tTp06SpCVLlnDTYQAAYMpXYAsODta+ffvyNHffvn0KDg4uUFO5ad++vbl95MiRHOOGYWjVqlWSpLp166ply5YO67Rs2VJ16tSRJK1atUrZFxsPHTqkAwcOSMoIVN7e3g7rZD006yiwffHFF0pPT5ckPf30086+LbNOenq6vvjiC6fzAABA6ZKvwNaxY0d9/PHHOnv2rMt5Z8+e1ccff6w///nPN9WcM1lX7hytxB09etQ8Fy4iIsJlrczxP/74Q8eOHbMby3o1qqs6QUFBql27tiRp69atOcbzWifrmKM6AACgdMpXYBszZoyuXr2q+++/Xzt27HA4Z8eOHerQoYOuXr2ql156qVCazG7z5s3mdr169XKM79+/39yuW7euy1pZxzNX026mzokTJ3TlyhWHdQICAhQUFOS0RtWqVc1j2Nl7AQAApVe+bpxbs2ZNRUVFqU+fPrrvvvtUs2ZNNWzYUH5+fkpKStK+fft05MgReXt769NPP1VYWFihN5yenq433njDfN27d+8cc7JeZZnb80xDQ0PN7RMnTtx0HcMwdPLkSfNQa9Y6eXm2amhoqH7++eccvWSVmppqt8qYmJiYa10AAHD7yvezRLt06aL//ve/Gjx4sK5evaro6GgtWbJE0dHRSk5O1qBBg/TTTz+pW7duRdGv3n33Xe3cuVNSxi02mjRpkmNOUlKSue3r6+uyno+Pj7l9+fLlIq2TW42sdbLXyGrKlCkKCAgwv7KGTgAAUPIU6NFU1atXN59skJSUpMTERPn7+8vPz6+w+7OzefNmjR07VpJUpUoVzZ071+G8q1evmtu5XdXq6elpbqekpBRpnbxcYZtZJ3uNrMaNG6cXXnjBfJ2YmEhoAwCgBCvws0Qz+fn5FXlQk6Sff/5ZkZGRSktLU7ly5bRs2TJVqVLF4dxy5cqZ29euXXNZN+uhxey3/sheJ+vr/NZJTk7OtZesdbLXyMrT09MuIAIAgJIt34dEi8PRo0f1wAMP6OLFiypTpow+/fRThzfLzZQ1QLo6tCjJ7gKB7IcsC7tObjWy1snL4VMAAFA6WD6wnTp1Sh07dtSpU6dks9m0aNEi9ejRw+U+WU/uz+0xT1lP7s9+WLEgdWw2W46LCzJf5+WRU5l1OMQJAAAyWTqwxcfH689//rN+++03SRlPLOjXr1+u+9WvX9/cPnjwoMu5Wcez3yKkIHVCQ0PtLkDIWichIcHlEwxOnz5tXvHp6HYlAACgdLJsYEtISFCnTp3Me5i98cYbGjZsWJ72rVGjhvmUhaz3bHMkJibj+Zh33nmnqlevbjfWunVrc9tVnbi4OPPZpq1atcoxntc6Wccc1QEAAKWTJQNbcnKyunTpoj179kiSXnnlFY0ZMybP+9tsNvOw6cGDB7V9+3aH87Zv326ujPXo0UM2m81uvHbt2uZKV1RUlJKTkx3Wyfqg9sjIyBzj3bt3l5tbxo/6ww8/dNp3Zh03Nzd1797d6TwAAFC6WC6wXbt2TZGRkeajmUaMGKF//vOf+a4zcuRI87FVw4cPz3GbjJSUFA0fPlyS5O7urpEjRzqs8+KLL0qSLly4oNGjR+cYP3LkiKZMmSIp40HwjgJbUFCQnnjiCUnS+vXrHT6wftmyZVq/fr0k6cknn3T5RAQAAFC62IzsTzwvZg8//LBWrFghSbr//vs1ffr0HCtfWXl4eJjP8cxu3Lhx5lMRGjdurDFjxigsLExHjhzR1KlT9cMPP5jzXn/9dYc1bty4oYiICDNAPvzwwxo0aJAqVKignTt3avLkyTp79qzc3Ny0evVqPfjggw7rnDhxQk2aNNG5c+fk7u6uUaNGqWvXrpKk1atX65133lFaWpoCAwO1Z8+ePD0VIVNiYqICAgKUkJBgPtqqKMzdFVNktYHb1dBmzq9Yv52kpHxb3C0AluPl1aFI6+fn77flApurcOZItWrVcjy0PVN6eroGDRqkRYsWOd1/wIABmj9/vnnI0pH4+Hh17txZu3btcjju6empWbNmaeDAgS573bFjh3r27On0woOgoCBFR0erRYsWLutkR2ADig+BDSi5rBTYLHdItDC5ublp4cKFWrNmjXr06KHg4GB5eHgoODhYPXr00Nq1a7VgwQKXYU2SKleurG3btmnOnDlq3bq1KlWqpHLlyqlmzZoaNGiQdu/enWtYk6QWLVpo7969Gj9+vBo0aCBfX1/5+vqqYcOGGj9+vPbt25fvsAYAAEo+y62wIf9YYQOKDytsQMnFChsAAADyjMAGAABgcQQ2AAAAiyOwAQAAWByBDQAAwOIIbAAAABZHYAMAALA4AhsAAIDFEdgAAAAsjsAGAABgcQQ2AAAAiyOwAQAAWByBDQAAwOIIbAAAABZHYAMAALA4AhsAAIDFEdgAAAAsjsAGAABgcQQ2AAAAiyOwAQAAWByBDQAAwOIIbAAAABZHYAMAALA4AhsAAIDFEdgAAAAsjsAGAABgcQQ2AAAAiyOwAQAAWByBDQAAwOIIbAAAABZHYAMAALA4AhsAAIDFEdgAAAAsjsAGAABgcQQ2AAAAiyOwAQAAWByBDQAAwOIIbAAAABZHYAMAALA4AhsAAIDFEdgAAAAsjsAGAABgcQQ2AAAAiyOwAQAAWByBDQAAwOIIbAAAABZHYAMAALA4AhsAAIDFEdgAAAAsjsAGAABgcQQ2AAAAiyOwAQAAWByBDQAAwOIIbAAAABZHYAMAALA4AhsAAIDFEdgAAAAsjsAGAABgcZYMbGfPntXq1as1YcIEPfjgg6pcubJsNptsNpueeuqpfNdbt26dIiMjFRISIk9PT4WEhCgyMlLr1q3Lc420tDS9//77atOmjQIDA+Xl5aWwsDANGTJEP//8c57rxMfHa8KECWrUqJH8/f3l7++vRo0aacKECTp//ny+vzcAAFDy2QzDMIq7iexsNpvTsf79+2vx4sV5qpOenq7Bgwdr4cKFTucMHDhQ8+bNk5ub8+waHx+vzp07a9euXQ7HPT09NWvWLA0cONBlPzt27FDPnj0VFxfncLxq1aqKjo5W8+bNXdbJLjExUQEBAUpISJC/v3++9s2Pubtiiqw2cLsa2qxtcbdQKFJSvi3uFgDL8fLqUKT18/P325IrbFndddddeuCBBwq07yuvvGKGtcaNG2vp0qXauXOnli5dqsaNG0uSFixYoPHjxzutcePGDUVGRpph7aGHHtK6deu0Y8cOzZgxQ1WqVFFqaqqGDBnicsXuxIkT6tatm+Li4uTu7q7Ro0crJiZGMTExGj16tNzd3XX69Gl169ZNJ0+eLND3CwAASiZLrrC9+uqratasmZo1a6Y77rhDx44dU40aNSTlfYXt0KFDuvvuu5WWlqamTZsqJiZGXl5e5nhycrIiIiIUGxsrd3d3HThwQOHh4TnqLFq0SAMGDJAkPfPMM5o9e7bd+OHDh9WkSRMlJiYqPDxcBw4ckLu7e446/fr105IlSyRJUVFReuSRR+zGo6Ki9Oijj+bre8zEChtQfFhhA0ouVthyMWnSJHXt2lV33HFHgWtMnz5daWlpkqSZM2fahTVJ8vb21syZMyVlnJ/27rvvOqzz9ttvS5IqVqyot956K8d4eHi4xo0bJykjvK1cuTLHnLi4OH3yySeSpE6dOuUIa5LUu3dvderUSZK0ZMkSp4dNAQBA6WPJwHazDMPQqlWrJEl169ZVy5YtHc5r2bKl6tSpI0latWqVsi82Hjp0SAcOHJCUEai8vb0d1sl6IYSjwPbFF18oPT1dkvT000877TuzTnp6ur744gun8wAAQOlSIgPb0aNHderUKUlSRESEy7mZ43/88YeOHTtmN7Zly5Yc8xwJCgpS7dq1JUlbt27NMZ7XOlnHHNUBAAClU4kMbPv37ze369at63Ju1vHM1bSbqXPixAlduXLFYZ2AgAAFBQU5rVG1alXzGHb2XgAAQOlVIgNb1qssQ0JCXM4NDQ01t0+cOHHTdQzDyHGVZ+br3GpkrZO9l6xSU1OVmJho9wUAAEquEhnYkpKSzG1fX1+Xc318fMzty5cvF2md3GpkrZO9RlZTpkxRQECA+ZU1dAIAgJKnRAa2q1evmtseHh4u53p6eprbKSkpRVontxpZ62SvkdW4ceOUkJBgfrlajQMAALe/nDcMKwHKlStnbl+7ds3l3NTUVHM7+60/stfJ+jq/dZKTk3PtJWud7DWy8vT0tAuIAACgZCuRK2x+fn7mtqtDi5LsLhDIfsiysOvkViNrnbwcPgUAAKVDiQxsWU/uz+0xT1kPJ2Y/F6wgdWw2W46LCzJf5+WRU5l1OC8NAABkKpGBrX79+ub2wYMHXc7NOl6vXr2brhMaGmp3AULWOgkJCS6fYHD69Gnzis/svQAAgNKrRAa2GjVqKDg4WJK0efNml3NjYjKej3nnnXeqevXqdmOtW7c2t13ViYuL06FDhyRJrVq1yjGe1zpZxxzVAQAApVOJDGw2m009evSQlLHytX37dofztm/fbq6M9ejRQzabzW68du3a5kpXVFSUkpOTHdbJ+qD2yMjIHOPdu3eXm1vGj/rDDz902ndmHTc3N3Xv3t3pPAAAULqUyMAmSSNHjlSZMmUkScOHD89xm4yUlBQNHz5ckuTu7q6RI0c6rPPiiy9Kki5cuKDRo0fnGD9y5IimTJkiKeNB8I4CW1BQkJ544glJ0vr16/X555/nmLNs2TKtX79ekvTkk0+6fCICAAAoXSx5W48tW7bo8OHD5uv4+Hhz+/Dhw3YrWpL9w9cz1a5dWy+99JLeeOMNxcbGqlWrVhozZozCwsJ05MgRTZ06VT/88IMk6aWXXlKtWrUc9tK/f38tWrRIW7du1ezZsxUXF6dBgwapQoUK2rlzpyZPnqzExES5ublpxowZcnd3/CN97bXX9NVXX+ncuXPq06ePYmNj1bVrV0nS6tWr9c4770iSAgMD9c9//jPPPysAAFDy2QzDMIq7ieyeeuopffTRR3me7+xbSE9P16BBg7Ro0SKn+w4YMEDz5883D1k6Eh8fr86dO2vXrl0Oxz09PTVr1iwNHDjQZZ87duxQz549nV54EBQUpOjoaLVo0cJlnewSExMVEBCghIQE81mkRWHurpgiqw3croY2a1vcLRSKlJRvi7sFwHK8vDoUaf38/P0usYdEpYxzwRYuXKg1a9aoR48eCg4OloeHh4KDg9WjRw+tXbtWCxYscBnWJKly5cratm2b5syZo9atW6tSpUoqV66catasqUGDBmn37t25hjVJatGihfbu3avx48erQYMG8vX1la+vrxo2bKjx48dr3759+Q5rAACg5LPkChvyhxU2oPiwwgaUXKywAQAAIM8IbAAAABZHYAMAALA4AhsAAIDFEdgAAAAsjsAGAABgcQQ2AAAAiyOwAQAAWByBDQAAwOIIbAAAABZHYAMAALA4AhsAAIDFEdgAAAAsjsAGAABgcQQ2AAAAiyOwAQAAWByBDQAAwOIIbAAAABZHYAMAALA4AhsAAIDFEdgAAAAsjsAGAABgcQQ2AAAAiyOwAQAAWByBDQAAwOIIbAAAABZHYAMAALA4AhsAAIDFEdgAAAAsjsAGAABgcQQ2AAAAiyOwAQAAWByBDQAAwOIIbAAAABZHYAMAALA4AhsAAIDFEdgAAAAsjsAGAABgcQQ2AAAAiyOwAQAAWByBDQAAwOIIbAAAABZHYAMAALA4AhsAAIDFEdgAAAAsjsAGAABgcQQ2AAAAiyOwAQAAWByBDQAAwOIIbAAAABZHYAMAALA4AhsAAIDFEdgAAAAsjsAGAABgcQQ2AAAAiyOwAQAAWByBDQAAwOIIbAAAABZHYAMAALA4AlsxOH78uEaNGqW6devKx8dHFStWVLNmzfTWW28pOTm5uNsDAAAW417cDZQ2X375pfr27avExETzveTkZMXGxio2NlYLFizQmjVrFB4eXoxdAgAAK2GF7Rb64Ycf9OijjyoxMVG+vr567bXXtG3bNn377bcaNGiQJOnQoUPq0qWLkpKSirlbAABgFayw3UIjRoxQSkqK3N3d9Z///Ef33nuvOXb//ferVq1aGj16tA4dOqR33nlHEydOLL5mAQCAZbDCdovs3LlT3333nSRpwIABdmEt06hRo1SvXj1J0nvvvafr16/f0h4BAIA1EdhukejoaHP76aefdjjHzc1N/fr1kyRdunRJGzduvBWtAQAAiyOw3SJbtmyRJPn4+KhJkyZO50VERJjbW7duLfK+AACA9RHYbpEDBw5IksLDw+Xu7vzUwbp16+bYBwAAlG5cdHALXL16VfHx8ZKkkJAQl3MrVKggHx8fXblyRSdOnHA4JzU1VampqebrhIQESbK7VUhRSLl8pUjrA7ejov69u1VSUvj9BrK7fr1of78z//0wDCPXuQS2WyDrLTp8fX1znZ8Z2C5fvuxwfMqUKZo0aVKO90NDQwveJIACGVXcDQC47SUlJSkgIMDlHALbLXD16lVz28PDI9f5np6ekqSUlBSH4+PGjdMLL7xgvk5PT9eFCxdUqVIl2Wy2m+wWVpeYmKjQ0FCdOHFC/v7+xd0OgELE73fpYhiGkpKSFBwcnOtcAtstUK5cOXP72rVruc7PPNzp5eXlcNzT09MMdZnKly9f8AZxW/L39+cfdKCE4ve79MhtZS0TFx3cAn5+fua2s8OcWV25knEuSV4OnwIAgJKPwHYLlCtXTpUqVZIknTx50uXcixcvmoGNc9IAAIBEYLtl6tevL0k6fPiw0tLSnM47ePCguZ351AMgK09PT7366qs5DosDuP3x+w1nCGy3SOvWrSVlHO7cvXu303mbN282t1u1alXkfeH24+npqYkTJ/IPOlAC8fsNZwhst0jPnj3N7Q8//NDhnPT0dH388ceSMi4iaN++/a1oDQAAWByB7RZp3ry52rRpI0lauHChvv/++xxz3nnnHfPpBiNGjFDZsmVvaY8AAMCabEZebq+LQvHDDz+oVatWSklJka+vr15++WW1b99eKSkp+vTTTzV//nxJUu3atRUbG2t3dSkAACi9CGy32Jdffqm+ffs6fZxN7dq1tWbNGoWHh9/iznA7qF69uo4fP67+/ftr8eLFxd0OAOAW4ZDoLdatWzf997//1fPPP6/atWvL29tb5cuXV9OmTTV16lT98MMPhLXbwKZNm2Sz2fL8RbgCbk/Hjh3L1++6sy/gZvGkg2JQrVo1TZs2TdOmTSvuVgAAwG2AwAbcpKFDh+qZZ55xOSckJOQWdQOgMN15553au3ev0/GGDRtKkpo2ber0DgBAYSCwATepSpUqatCgQXG3AaAIlC1bNk+/3z4+Pvw7gCLFOWwAAAAWR2ADbpErV67os88+08CBA3XPPfcoICBAZcuWVWBgoCIiIvT222/r8uXLN/UZV69e1YwZM9SuXTsFBgaqbNmyqlixourUqaMHH3xQ06ZN07Fjx5zuf+PGDX300Ufq2rWrgoOD5enpqUqVKql169aaNm2aUlJSbqo/oDRp166dbDab2rVrJ0n69ddf9eyzz6pWrVry9vaWzWYzfx8XL15sXqDg6nc060UQuV3MFB0drUceeUR33XWXypUrZ17gNmnSJF28eLFwvkncMhwSBW6RLl262D16LFN8fLxiYmIUExOjOXPmaO3atapbt26+658+fVodO3bU/v377d6/ePGiLl68qEOHDumrr77SqVOn9Pbbb+fY//fff1f37t31008/2b1/4cIFbd26VVu3btXcuXO1Zs0a1a5dO9/9AaXZqlWr9MQTT+jKlStF/lkXL15Ur169tGHDBrv3U1NTtXv3bu3evVtz5szRqlWr1LJlyyLvB4WDwAbcImlpaWrYsKG6d++upk2bKjg4WIZh6Pjx41q5cqWioqJ09OhR9ezZUz/++KPKlSuXr/rDhw83w1rfvn310EMPKTg4WGXKlNHp06cVGxurVatWOdz3/Pnzat26tU6cOCFPT08NGjRIERERql69ui5fvqz//Oc/eu+993T48GE9+OCD2rNnjwICAm76ZwKUBr///rv69u0rb29v/f3vf1ebNm1UpkwZ7dq1S76+voX6WampqerYsaP27NmjMmXK6PHHH1fnzp1Vo0YNXb9+XTExMZo2bZrOnj2rzp0764cfflC1atUKtQcUEQNAvm3cuNGQZEgyhg4dauzdu9fp15kzZwzDMIxDhw65rPn1118bbm5uhiRjwYIFDudUq1bNkGT079/f7v2UlBSjbNmyhiRj1KhRLj/n/PnzOd57/PHHDUlGtWrVjN9++83hfnv27DF8fHwMScbLL7/s8jOA0iLz34GIiIgcYxEREeZ4cHCwcfz4cad1PvzwQ3Pu0aNHnc47evSoOe/DDz/MMf7yyy8bkozy5csbsbGxDmscO3bMqFq1qiHJePzxx3P7FmERnMMG3KS5c+eqYcOGTr/mzJkjSapVq5bLOh07dlT37t0lZZx7kh8XLlzQ9evXJUlt27Z1ObdixYp2r48dO6bPPvtMkjRr1izVqFHD4X6NGzfWsGHDJIkbAQP59MYbb+iuu+4q0s+4fPmyZs+eLUmaPHmymjRp4nBetWrV9Pe//12StGzZsltymBY3j8AGFJNz587p119/1b59+8yvwMBAScpxHlluKlWqJA8PD0nSkiVLlJaWlud916xZoxs3bsjb21sPPvigy7mZYfDUqVP6/fff89UjUFp5eHjokUceKfLP2bx5sxISEiRJvXr1cjk383f5+vXr2r17d5H3hpvHOWzATXr11Vc1ceLEPM3dunWrZsyYoW+++UYXLlxwOi8+Pj5fPXh6eurRRx/VkiVL9Pnnn2vXrl3q3bu32rVrp/vuu0/ly5d3um9sbKwkKTk5We7uef8nIS4urshXDICSoFatWvk+J7UgMn+XJalq1ap53i8uLq4o2kEhY4UNuEUmTpyo1q1bKyoqymVYk1Sg22fMmjVL3bp1kyQdP35cb731lrp06aJKlSqpWbNmeuutt8z/+s7q7Nmz+f4sKSPgAchdhQoVbsnn8LtcsrHCBtwC3377rSZNmiRJqlmzpl588UW1bt1ad911l3x8fMyVrQkTJmjy5MkF+gx/f3998cUX2rlzp6KiorRp0yb9+OOPunHjhmJjYxUbG6u3335b0dHRuvfee839bty4IUmqXLmyNm7cmOfPc3auGwB7ZcqUuSWfk/m7LEl79uxR2bJl87Qfj867PRDYgFvggw8+kJTxX9rbt283z1XLLreVt7xo3ry5mjdvLklKSkrSpk2btHjxYq1YsUJnz57Vww8/rCNHjsjLy0tSxvlvmXPr1at3y/64APj/3Nz+/wGv9PR0p/NcXSCQ+bssSYGBgQSxEoZDosAt8PPPP0uS2rdv7zSsSfbnoBQGPz8/devWTcuXL9dzzz0nKeMGu1u2bDHnNG7cWFLG/ZsK+/MB5I2fn5+57eopBIcOHXI6lvm7LGWcL4uShcAG3AKZV226+q/jH374QTt27CiyHjp06GBuZ72ooVu3brLZbJKk6dOnF9nnA3Au6ykGrv7DaenSpU7HOnbsKG9vb0nSjBkzZBhG4TWIYkdgA26BzHuwbdmyRYcPH84xfu7cOT355JMFrv/bb785fOxVVv/5z3/M7ax/HOrUqWPecuDTTz/VtGnTXNY5evSoyz8aAPKvQYMG5j0SZ82apdTU1BxzoqKitGzZMqc1ypcvr2effVaStG3bNj3//PMuD6+eOXNGCxYsuMnOcatwDhtwC/Tr109ffvmlrly5ooiICI0dO9a8qeW2bds0bdo0xcXF6d5779X333+f7/q///672rdvr/r16ysyMlJNmzbVnXfeKUk6ceKEPvvsM0VFRUmS7rnnHrVo0cJu/7lz5yo2Nla//fabRo0apVWrVqlfv366++675enpqfPnz+unn37SV199pQ0bNigyMlJ9+vS5yZ8KgEzu7u4aMmSIpkyZon379un+++/X6NGjddddd+nMmTNatmyZFi9erPvuu0/btm1zWucf//iHNm/erB07dui9997Tpk2bNGjQIN1zzz3y8fHRxYsX9fPPP+ubb77RunXr1LBhQw0cOPAWfqcoKAIbcAv06tVLTz/9tD788EOdOnXKPJ8sU5kyZfTuu+/q4sWLBQpsmfbv35/j4e9Z1a1bVytWrDAPgWaqWLGitm7dqt69e+u7774zH0bvjL+/f4F7BODY+PHjtXHjRm3fvl3btm1Tz5497cbbtWunWbNmqUGDBk5reHp66uuvv9ZTTz2lFStW6KeffjJX3Rzhd/n2wSFR4BZZtGiRlixZojZt2sjPz0+enp6qVq2annzySW3btk0jRowocO02bdpo06ZNGjdunNq3b6/w8HD5+fmpbNmyuuOOO/TAAw/o/fff148//uj0dhxBQUGKiYnR6tWr9cQTT6hmzZry9vZW2bJlFRgYqPvuu0+jRo3S5s2btWjRogL3CsAxb29vbdiwQa+99poaNmwoLy8v+fv7q1mzZpo1a5a++eYb+fj45FrHz89Py5cv13fffaeBAweqTp068vPzk7u7uypWrKhmzZpp2LBhWrt2rb7++utb8J2hMNgMzkoEAACwNFbYAAAALI7ABgAAYHEENgAAAIsjsAEAAFgcgQ0AAMDiCGwAAAAWR2ADAACwOAIbAACAxRHYAAAALI7ABgAAYHEENgAAAIsjsAEAisSmTZtks9m0adOm4m4FuO0R2ACUaDabLU9fpTlUvP7664qOji7uNgC44F7cDQBAUVqyZInd648//lhff/11jvfr1at3K9uylNdff129evVSz549i7sVAE4Q2ACUaH379rV7vX37dn399dc53s8uOTlZ3t7eRdlasTIMQ1evXpWXl1dxtwIgDzgkCqDUa9eunRo0aKDdu3erbdu28vb21ssvvyxJWrVqlbp06aLg4GB5enoqLCxMkydP1o0bNxzW2L9/v9q3by9vb2/deeedevPNN3N83syZM3X33XfL29tbFSpUUNOmTfXvf//bHJ84caJsNpsOHjyo3r17y9/fX5UqVdKIESN09epVu1ppaWmaPHmywsLC5OnpqerVq+vll19Wamqq3bzq1aura9euWr9+vZo2bSovLy/NmzdPNptNV65c0UcffWQeHn7qqafM/f744w/99a9/1R133CFPT0/dfffdWrRoUY7v6eTJk+rZs6d8fHxUpUoVPf/88zl6AFBwrLABgKTz58/rwQcf1GOPPaa+ffvqjjvukCQtXrxYvr6+euGFF+Tr66sNGzZowoQJSkxM1FtvvWVX4+LFi/rLX/6ihx56SL1799bnn3+uMWPGqGHDhnrwwQclSR988IGee+459erVywxg//3vf7Vjxw49/vjjdvV69+6t6tWra8qUKdq+fbtmzJihixcv6uOPPzbnDBw4UB999JF69eqlUaNGaceOHZoyZYoOHDiglStX2tX75Zdf1KdPHw0ZMkSDBg1SnTp1tGTJEg0cOFDNmzfX4MGDJUlhYWGSpDNnzqhly5ay2Wx69tlnFRgYqHXr1mnAgAFKTEzUyJEjJUkpKSnq0KGDfv/9dz333HMKDg7WkiVLtGHDhsL7Pwgo7QwAKEWGDRtmZP+nLyIiwpBkvP/++znmJycn53hvyJAhhre3t3H16tUcNT7++GPzvdTUVCMoKMh4+OGHzfd69Ohh3H333S57fPXVVw1JRvfu3e3ef+aZZwxJxk8//WQYhmH8+OOPhiRj4MCBdvNefPFFQ5KxYcMG871q1aoZkoyvvvoqx+f5+PgY/fv3z/H+gAEDjKpVqxrx8fF27z/22GNGQECA+bOZPn26IcmIiooy51y5csUIDw83JBkbN250+f0CyB2HRAFAkqenp55++ukc72c9xyspKUnx8fFq06aNkpOTdfDgQbu5vr6+dufGeXh4qHnz5vrtt9/M98qXL6+TJ09q165dufY0bNgwu9fDhw+XJK1du9buf1944QW7eaNGjZIkrVmzxu79GjVqqFOnTrl+rpRxjtvy5cvVrVs3GYah+Ph486tTp05KSEjQnj17zD6qVq2qXr16mft7e3ubK3YAbh6BDQAk3XnnnfLw8Mjx/s8//6zIyEgFBATI399fgYGBZihLSEiwmxsSEiKbzWb3XoUKFXTx4kXz9ZgxY+Tr66vmzZurVq1aGjZsmLZu3eqwp1q1atm9DgsLk5ubm44dOyZJOn78uNzc3BQeHm43LygoSOXLl9fx48ft3q9Ro4aLn4C9c+fO6dKlS5o/f74CAwPtvjKD7dmzZ80+wsPDc3zvderUyfPnAXCNc9gAQHJ4teSlS5cUEREhf39//eMf/1BYWJjKlSunPXv2aMyYMUpPT7ebX6ZMGYe1DcMwt+vVq6dffvlFq1ev1ldffaXly5drzpw5mjBhgiZNmuSyx+yBKLf3s8vPFaGZ31vfvn3Vv39/h3MaNWqU53oAbg6BDQCc2LRpk86fP68VK1aobdu25vtHjx69qbo+Pj569NFH9eijj+ratWt66KGH9Nprr2ncuHEqV66cOe/XX3+1WxU7fPiw0tPTVb16dUlStWrVlJ6erl9//dXuPnJnzpzRpUuXVK1atTz14yjwBQYGys/PTzdu3FDHjh1d7l+tWjXt27dPhmHY1frll1/y9PkAcschUQBwInPFLOsK2bVr1zRnzpwC1zx//rzdaw8PD9WvX1+GYej69et2Y7Nnz7Z7PXPmTEkyrzjt3LmzJGn69Ol286ZNmyZJ6tKlS5568vHx0aVLl+zeK1OmjB5++GEtX75c+/bty7HPuXPnzO3OnTvr1KlT+vzzz833kpOTNX/+/Dx9PoDcscIGAE7cd999qlChgvr376/nnntONptNS5YssQtw+fXAAw8oKChIrVq10h133KEDBw5o1qxZ6tKli/z8/OzmHj16VN27d9df/vIXff/99/rXv/6lxx9/XH/6058kSX/605/Uv39/zZ8/3zx8u3PnTn300Ufq2bOn2rdvn6eemjRpom+++UbTpk1TcHCwatSooRYtWuiNN97Qxo0b1aJFCw0aNEj169fXhQsXtGfPHn3zzTe6cOGCJGnQoEGaNWuW+vXrp927d6tq1apasmRJib7xMHDLFeMVqgBwyzm7rYezW21s3brVaNmypeHl5WUEBwcbo0ePNtavX5/jdhXOavTv39+oVq2a+XrevHlG27ZtjUqVKhmenp5GWFiY8dJLLxkJCQnmnMzbeuzfv9/o1auX4efnZ1SoUMF49tlnjZSUFLv6169fNyZNmmTUqFHDKFu2rBEaGmqMGzfO7pYjhpFxW48uXbo4/B4PHjxotG3b1vDy8jIk2d3i48yZM8awYcOM0NBQo2zZskZQUJDRoUMHY/78+XY1jh8/bnTv3t3w9vY2KleubIwYMcL46quvuK0HUEhshnET/6kIACh0EydO1KRJk3Tu3DlVrly5uNsBYAGcwwYAAGBxBDYAAACLI7ABAABYHOewAQAAWBwrbAAAABZHYAMAALA4AhsAAIDFEdgAAAAsjsAGAABgcQQ2AAAAiyOwAQAAWByBDQAAwOL+H4sJZ0poXTr6AAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "f = sns.countplot(x='Transported', data=train, palette='Set3')\n",
    "f.set_xlabel(\"Transported\", fontsize=12)\n",
    "f.set_ylabel(\"Count\", fontsize=12)\n",
    "## 分布均匀，metrics选 KFold + accuracy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PassengerId       0\n",
       "HomePlanet      201\n",
       "CryoSleep       217\n",
       "Cabin           199\n",
       "Destination     182\n",
       "Age             179\n",
       "VIP             203\n",
       "RoomService     181\n",
       "FoodCourt       183\n",
       "ShoppingMall    208\n",
       "Spa             183\n",
       "VRDeck          188\n",
       "Name            200\n",
       "Transported       0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 2000x500 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "f, [ax1, ax2, ax3] = plt.subplots(1, 3, figsize=(20, 5))\n",
    "sns.countplot(x='VIP', hue='Transported', data=train, ax=ax1)\n",
    "sns.countplot(x='CryoSleep', hue='Transported', data=train, ax=ax2)\n",
    "sns.countplot(x='HomePlanet', hue='Transported', data=train, ax=ax3)\n",
    "ax1.set_title(\"VIP Feature\")\n",
    "ax2.set_title(\"CryoSleep Feature\")\n",
    "ax3.set_title(\"HomePlanet Feature\")\n",
    "f.suptitle(\"Featrues\", size=20, y=1.1)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(False    8291\n",
       " True      199\n",
       " Name: VIP, dtype: int64,\n",
       " False    4110\n",
       " True       74\n",
       " Name: VIP, dtype: int64)"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.VIP.value_counts(),  test.VIP.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0    8291\n",
       " 1     199\n",
       " Name: VIP, dtype: int64,\n",
       " 0    4110\n",
       " 1      74\n",
       " Name: VIP, dtype: int64)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.loc[train['VIP'] == False, 'VIP'] = 0\n",
    "train.loc[train['VIP'] == True, 'VIP'] = 1\n",
    "test.loc[test['VIP'] == False, 'VIP'] = 0\n",
    "test.loc[test['VIP'] == True, 'VIP'] = 1\n",
    "\n",
    "train.VIP.value_counts(),  test.VIP.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0    8494\n",
       " 1     199\n",
       " Name: VIP, dtype: int64,\n",
       " 0    4203\n",
       " 1      74\n",
       " Name: VIP, dtype: int64)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.VIP.fillna(0, inplace=True)\n",
    "test.VIP.fillna(0, inplace=True)\n",
    "train.VIP.value_counts(),  test.VIP.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "False    0.641694\n",
      "True     0.358306\n",
      "Name: CryoSleep, dtype: float64\n",
      "False    0.630975\n",
      "True     0.369025\n",
      "Name: CryoSleep, dtype: float64\n",
      "============================\n",
      "0    0.650638\n",
      "1    0.349362\n",
      "Name: CryoSleep, dtype: float64\n",
      "0    0.638999\n",
      "1    0.361001\n",
      "Name: CryoSleep, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "print(train.CryoSleep.value_counts(normalize=True))\n",
    "print(test.CryoSleep.value_counts(normalize=True))\n",
    "train.loc[train['CryoSleep'] == False, 'CryoSleep'] = 0\n",
    "train.loc[train['CryoSleep'] == True, 'CryoSleep'] = 1\n",
    "test.loc[test['CryoSleep'] == False, 'CryoSleep'] = 0\n",
    "test.loc[test['CryoSleep'] == True, 'CryoSleep'] = 1\n",
    "train.CryoSleep.fillna(0, inplace=True)\n",
    "test.CryoSleep.fillna(0, inplace=True)\n",
    "print(\"============================\")\n",
    "print(train.CryoSleep.value_counts(normalize=True))\n",
    "print(test.CryoSleep.value_counts(normalize=True))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TRAPPIST-1e      0.680433\n",
      "55 Cancri e      0.207063\n",
      "PSO J318.5-22    0.091568\n",
      "NONE             0.020936\n",
      "Name: Destination, dtype: float64\n",
      "TRAPPIST-1e      0.691139\n",
      "55 Cancri e      0.196633\n",
      "PSO J318.5-22    0.090718\n",
      "NONE             0.021510\n",
      "Name: Destination, dtype: float64\n",
      "===============================\n",
      "TRAPPIST-1e      5915\n",
      "55 Cancri e      1800\n",
      "PSO J318.5-22     978\n",
      "Name: Destination, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "train.Destination.fillna('NONE', inplace=True)\n",
    "test.Destination.fillna('NONE', inplace=True)\n",
    "print(train.Destination.value_counts(normalize=True))\n",
    "print(test.Destination.value_counts(normalize=True))\n",
    "print(\"===============================\")\n",
    "train.loc[train['Destination'] == 'NONE', 'Destination'] = 'PSO J318.5-22'\n",
    "test.loc[test['Destination'] == 'NONE', 'Destination'] = 'PSO J318.5-22'\n",
    "print(train.Destination.value_counts())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [],
   "source": [
    "train.to_csv('../input/train_clear.csv', index=False)\n",
    "test.to_csv('../input/test_clear.csv', index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "####### 已经做完的\n",
    "# 将布尔值进行转换，已经清理好了文件并保存下在 xx_clear.csv\n",
    "\n",
    "####### 准备做的\n",
    "# 不要name，使用one-hot进行编码，然后用LR计算模型"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Transported</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
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       "      <th>1</th>\n",
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       "      <th>2</th>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0021_01</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0023_01</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  PassengerId  Transported\n",
       "0     0013_01            1\n",
       "1     0018_01            0\n",
       "2     0019_01            1\n",
       "3     0021_01            0\n",
       "4     0023_01            0"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a = pd.read_csv('../output/submission.csv')\n",
    "a.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Transported</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0013_01</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0018_01</td>\n",
       "      <td>False</td>\n",
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       "      <th>2</th>\n",
       "      <td>0019_01</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0021_01</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0023_01</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  PassengerId Transported\n",
       "0     0013_01        True\n",
       "1     0018_01       False\n",
       "2     0019_01        True\n",
       "3     0021_01       False\n",
       "4     0023_01       False"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a.loc[a['Transported'].astype(int) == 1, 'Transported'] = True\n",
    "a.loc[a['Transported'].astype(int) == 0, 'Transported'] = False\n",
    "a.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "a.to_csv('../output/submission.csv', index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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